The present invention relates to an image processing technique for automatically and accurately detecting an anatomical configuration from a chest image necessary for computer-aided diagnosis of the chest, and in particular, to an image processing technique that makes it possible to detect a boundary of a ribcage from a chest image with higher precision, even when its image is poor in quality.
Digitized chest radiographs have been used widely in the field of computer-aided diagnosis. There have been known a wide variety of types of computer-aided diagnosis capable of automatically detecting ribcage boundary information and landmark information both specifying anatomical configurations of the chest. One conventional technique is provided by xe2x80x9cXin-Wei Xu and Kunio Doi, Image feature analysis for computer-aided diagnosis: Accurate determination of ribcage boundary in chest radiographs, Med. Phys. 22(5), May 1995.xe2x80x9d This technique is also provided by Japanese Patent Laid-open Publication NO.7-37074.
This diagnostic technique uses lesion-enhanced images in order to detect temporal changes of diseases such as lung diseases among digital chest images acquired at different times for the same patient""s region. To raise diagnostic accuracy, this technique comprises the steps of obtaining previous and current digital chest images, positioning both previous and current digital images by performing non-linear warping processing based on a non-linear warping technique on either the first or second digital image, and making a subtraction between previous and current images one of which (has undergone the non-linear warping. The non-linear warping technique uses information detected from a chest image in relation to its anatomical structure and is based on a local matching technique to be applied to a number of tiny regions of interest (ROIs) selected on the basis of the information. The non-linear warping technique is mapping of amounts of matching shift obtained between corresponding locations in two frames of images. The mapping is realized using amounts of local matching resulting from a local matching technique applied to the locations and a weighted fitting technique that uses weighting coefficients resulting from image data analysis applied to the ROIs. In addition, the mapping of shift amounts is based on two-dimensional polynomial functions fitted to shift values.
The above conventional automatic detection technique provides a ribcage boundary in the procedures detailed in FIG. 1.
First, as pre-processing for detecting a ribcage boundary from chest image data 40, information indicative of landmarks 41, which becomes landmark information in displaying a configuration of the chest (Step S50).
Then, based on the landmark information 41, a series of upper lung ribcage boundary candidate points is detected (Step S51). Based on both landmark information 41 and upper lung ribcage boundary candidate point series 42, a series 43 of right ribcage boundary candidate points and a series 44 of left ribcage boundary candidate points are both detected (Step S52).
Based on the landmark information 41, the upper lung ribcage boundary candidate point series 42 is approximated with polynomials, so that an upper lung ribcage boundary point series 45 consisting of a series of points continuously aligned in the X-coordinate direction (horizontal direction) (Step S53). Like this, based on the landmark information 41, both right and left ribcage boundary candidate point series 43 and 44 are approximated with polynomials, so that right and left ribcage boundary point series 46 and 47 each consisting of a series of points continuously aligned in the Y-coordinate direction (vertical direction) (Step S54).
Finally, on the basis of the landmark information 41, both upper lung ribcage boundary point series 45 and right ribcage boundary point series 46 are combined to obtain a right ribcage boundary, and both upper ribcage boundary point series 45 and left ribcage boundary point series 47 are combined to obtain a left ribcage boundary 48 (Step S55).
This conventional technique adopts the lung length of ⅕ as the landmark information 41 for obtaining the upper lung ribcage boundary point series 45.
However, as shown in FIG. 2, a region along the lung length of ⅕ within the lung field is relatively low in contrast and the original chest image includes artifacts, such as a blank 49 occurring in converting a chest image into a digital image by the use of a film digitizer and noises 50 occurring in image processing. Thus, in some occasions, the foregoing conventional technique fails in detecting search limit points to search a series of ribcage boundary candidate points.
FIGS. 3A and 3B pictorially show examples in which the detection fails. In the case of FIG. 3A, an artifact that consists of a noise 50 existing at the right side of a chest image has influence on setting an outer search limit point in such a way that a blade bone portion is set as the outer search limit point by mistake. As a result, as shown in FIG. 3B, the processing fails in detecting a series 51 of upper lung right ribcage boundary candidate points.
Specifically, in the case of the conventional detection technique, a fail in detection of ribcage boundary candidate points due to influence of setting a reference for detecting the ribcage boundary candidate points and artifacts on an original chest image results in that a ribcage boundary obtained by approximating polynomials to a series of the ribcage boundary candidate points deviates from the true ribcage boundary, thereby lowering accuracy in computer-aided diagnosis as a whole.
In addition, both right and left ribcage boundary candidate point series are detected from the series of upper lung ribcage boundary candidate points. Hence, failing in detecting the series of upper lung ribcage boundary candidate points will give rise to failure in successively performed detection of both series of right and left ribcage boundary candidate points. A ribcage boundary computed by approximating polynomials to such erroneous ribcage boundary candidate point series is no longer the true ribcage boundary. This will lead to lowered accuracy in computer-aided diagnosis.
For using in computer-aided diagnosis features of a chest image and positional information indicative of an anatomical structure, it is significant to acquire more accurate information about a ribcage boundary and landmarks. It has therefore been strongly desired that the more accurate information be available for computer-aided diagnosis.
An object of the present invention is to prevent erroneous detection of a ribcage boundary, which was seen in the conventional technique, thereby acquiring accurate ribcage boundary information that will not be influenced by quality of chest images.
In order to realize the above object, one aspect of the present invention is provided by a method for detecting a ribcage boundary for computer-aided diagnosis requiring anatomical structure information to be detected from a digital chest image. The method comprises the steps of obtaining a profile of smoothed pixel-value integrated averages in each of right and left lung field of the image; deciding a threshold for each of the right and left lung fields with the profile taken as a reference; and searching each of the right and left lung fields from a central part of each lung field outwardly on the chest image so as to determine a position exceeding the threshold, the position being set as an outer search limit point for a series of an upper lung ribcage boundary candidate points for the ribcage boundary.
Preferably, the searching step further includes the steps of calculating a first derivative of the profile for examining changes in pixel value within each of the right and left lung fields in cases the position exceeding the threshold is not found; searching not only a position from the central part of each of the right and left lung fields outwardly on the chest image within a predetermined search range, the position showing a maximum change in the first derivative, but also a position showing a maximal value in the profile within the search range; and deciding a median between the maximal value and the position showing the maximum change in the first derivative as the outer search limit point.
By performing this method, the outer search limit point can be determined with a higher precision, preventing a ribcage boundary from being detected erroneously.
According to the present invention, as another mode, there is provided a method for detecting a ribcage boundary for computer-aided diagnosis requiring anatomical structure information to be detected from a digital chest image, comprising the steps of setting a central position in each of right and left lung fields on the chest image, the central position being in the vicinity of a lung length of xc2xd, the central position in each field being symmetric right and left with regard to a midline regarded as a symmetric axis; and deciding a search starting point for each of series of right and left ribcage boundary candidate points from a central position of each of the right and left lung fields outwardly on the chest image.
Using this method, the search starting point for the right and left ribcage boundary candidate point series can be detected with precision even when a series of upper lung ribcage boundary candidate points was erroneously detected. This prevents a ribcage boundary from being detected with errors.
It is preferred that the deciding step includes a step of performing pre-processing for removing an artifact including at least one of a blank arising during a conversion into the digital chest image by use of a film digitizer and a noise (black or white noise) caused by image processing performed on the chest image. Thanks to the pre-processing, the detection of the search starting point will be free from various influences of the artifacts. Hence it is possible to provide information about a ribcage boundary with a more precision, which is independent on quality of chest images.
According to the present invention, another aspect, there is a system for detecting a ribcage boundary for computer-aided diagnosis requiring anatomical structure information to be detected from a digital chest image, comprising a unit for obtaining a profile of smoothed pixel-value integrated averages in each of right and left lung fields of the image, a unit for deciding a threshold for each of the right and left lung fields with the profile taken as a reference, and a unit for searching each of the right and left lung fields from a central part of each lung field outwardly on the chest image so as to determine a position exceeding the threshold, the position being set as an outer search limit point for a series of an upper lung ribcage boundary candidate points. This configuration also enables the outer search limit point to be determined with a higher precision, preventing a ribcage boundary from being detected erroneously.